Genetic differences according to onset age and lung function in asthma: A cluster analysis

Abstract Background The extent of differences between genetic risks associated with various asthma subtypes is still unknown. To better understand the heterogeneity of asthma, we employed an unsupervised method to identify genetic variants specifically associated with asthma subtypes. Our goal was to gain insight into the genetic basis of asthma. Methods In this study, we utilized the UK Biobank dataset to select asthma patients (All asthma, n = 50,517) and controls (n = 283,410). We excluded 14,431 individuals who had no information on predicted values of forced expiratory volume in one second percent (FEV1%) and onset age, resulting in a final total of 36,086 asthma cases. We conducted k‐means clustering based on asthma onset age and predicted FEV1% using these samples (n = 36,086). Cluster‐specific genome‐wide association studies were then performed, and heritability was estimated via linkage disequilibrium score regression. To further investigate the pathophysiology, we conducted eQTL analysis with GTEx and gene‐set enrichment analysis with FUMA. Results Clustering resulted in four distinct clusters: early onset asthmanormalLF (early onset with normal lung function, n = 8172), early onset asthmareducedLF (early onset with reduced lung function, n = 8925), late‐onset asthmanormalLF (late‐onset with normal lung function, n = 12,481), and late‐onset asthmareducedLF (late‐onset with reduced lung function, n = 6508). Our GWASs in four clusters and in All asthma sample identified 5 novel loci, 14 novel signals, and 51 cluster‐specific signals. Among clusters, early onset asthmanormalLF and late‐onset asthmareducedLF were the least correlated (r g = 0.37). Early onset asthmareducedLF showed the highest heritability explained by common variants (h 2 = 0.212) and was associated with the largest number of variants (71 single nucleotide polymorphisms). Further, the pathway analysis conducted through eQTL and gene‐set enrichment analysis showed that the worsening of symptoms in early onset asthma correlated with lymphocyte activation, pathogen recognition, cytokine receptor activation, and lymphocyte differentiation. Conclusions Our findings suggest that early onset asthmareducedLF was the most genetically predisposed cluster, and that asthma clusters with reduced lung function were genetically distinct from clusters with normal lung function. Our study revealed the genetic variation between clusters that were segmented based on onset age and lung function, providing an important clue for the genetic mechanism of asthma heterogeneity.


| INTRODUCTION
Asthma is a complex respiratory disease characterized by nonspecific bronchial hyperresponsiveness and airway inflammation, and it exhibits a wide range of clinical and pathophysiological features. [1][2][3][4][5][6] As such, defining asthma subtypes via genetic approaches can help improve our understanding of the disease and lead to better management and treatment strategies. 2 Severe asthma, in particular, poses a significant economic burden on the healthcare system, and pharmacological management of severe asthma remains a major challenge. 7 Antibody-based therapies targeting the T-helper type 2 (Th2) inflammation pathway have been prescribed for patients with severe asthma 8 ; however, the heterogeneous nature of asthma emphasizes the need for genetic studies to identify subtype-specific targets. 9 Previous studies have attempted to dissect asthma subtypes using various criteria and have identified several genetic associations. 10 For instance, Ferreira et al. studied the genetic variation that was influenced by the onset age of asthma and identified a specific association between early onset asthma and the 17q21 locus. 11 Another study conducted by Shrine et al. specifically studied genetic risk components of moderate-to-severe asthma that were defined by the appropriate medication or the diagnosis by a doctor and identified three loci associated with moderate-tosevere asthma, including signals in KIAA1109, MUC5AC, and GATA3. 12 Although these studies have detected a number of genome-wide significant association signals, they have been limited by insufficient phenotypic dissection in explaining subtype-specific characteristics of asthma. On the other hand, Siroux et al. dissected asthma subtypes into four clusters in an unsupervised manner using the latent class analysis and performed a genetic association analysis on each cluster. 9 However, this clustering study identified only two genome-wide significant associations, indicating limited power due to the small sample size of 3001 asthma cases. 9 Therefore, with a larger sample size, the dissection of asthmatic phenotypes into various subtypes could better reveal the heterogeneous characteristics of asthma that could be explained by genetic variations.
In this study, we utilized the large sample size of asthma patients available in the UK Biobank to classify asthma subtypes through kmeans clustering based on two important variables: onset age and predicted FEV1% values. The primary objectives of our investigation were to identify (i) the differences in clinical phenotypes among asthma clusters, (ii) the specific genetic association signals in asthma clusters by performing cluster-specific genome-wide association studies (GWAS), and (iii) the genetic variations among clusters through heritability, genetic correlation, and gene-set enrichment analysis.

| Study population for the discovery set
The UK Biobank recruited a total of 488,266 participants from 2006 to 2010. 13 Ethical approval for data collection from participants was  Table S29). Among these, asthma cases (All asthma, n = 50,517) were defined as individuals diagnosed with asthma by a doctor (data field 22127 and 6152) or those who had reported their onset age (data field 3786). For the clustering analysis, we utilized information on predicted FEV1% and asthma onset age from the UK Biobank dataset. Predicted FEV1% was calculated based on the FEV1 value (data field 3063), using the NHANES III spirometry Caucasian reference panel. 14 Figure S1).
To further test the reproducibility of the identified signals, we selected asthma cases (n = 3071) from 64,584 unrelated individuals who self-reported asthma (data field 20002: non-cancer illness code, self-reported) after excluding 44,168 non-white British individuals (data field 21000: 1 or 1001 or 1002 or 1003). For clustering analysis, we selected asthma cases with information on asthma onset age and predicted FEV1% (n = 2592) to replicate the association with clusters identified in the discovery set. Similar to the discovery set, we divided the asthma cases into four groups using kmeans clustering: 467 in early onset asthma normalLF , 794 in early onset asthma reducedLF , 522 in late-onset asthma normalLF , and 809 in late-onset asthma reducedLF . We selected 13,285 controls as individuals who had not been diagnosed with bronchitis, emphysema, asthma, rhinitis, eczema, and allergy by a doctor (data field 6152). Due to the small size of the replication set, we performed a meta-analysis of the association results from both the unused UK Biobank sample clusters and the discovery sample clusters. We then applied three criteria to identify reproducible signals; P < 5E-8 in discovery clusters, p < 0.05 in replication clusters, and p values from the meta-analysis less than P from the discovery or replication clusters.

| Genotype data
In phase 1 of the UK Biobank (year 2015), the blood samples of 152,611 participants were genotyped using the Affymetrix Axiom (Affymetrx, Santa Clara, CA, USA) UK BiLEVE array. 15

| Statistical analysis
We utilized bi-variate data on asthma onset age and predicted FEV1% to perform k-means cluster analysis. Clustering and comparison of clusters were carried out using the SPSS 25 statistical package (SPSS Inc., Chicago, IL, USA). To analyse the differences in baseline characteristics such as lung function, smoking, comorbidity, medication use, and respiratory symptoms among clusters, we employed Kruskal-Wallis (for continuous variables) and Chi-square test (for categorical variables).
We conducted logistic regression analysis for GWAS using PLINK v.1.90 to identify cluster-specific genetic variants while assuming an additive genetic model. We adjusted for age, sex, body mass index (BMI), smoking status, and PC1-10. 17 We performed five separate GWASs: one using All asthma cases (n = 50,517) and four others for each cluster (early onset asthma normalLF , n = 8172; early onset asthma reducedLF , n = 8925; late-onset asthma normalLF , n = 12,481; and late-onset asthma reducedLF , n = 6508). We used 283,410 control samples for all GWASs. We set the genome-wide significance threshold to a p-value of 5.00 � 10 −8 . We compared differences in genetic variants between the clusters to identify cluster-specific genetic associations.
Additionally, to address potential issues of hidden ancestry and unbalanced case-control, we conducted the SAIGE analysis using our KIM ET AL.
-3 of 14 discovery study set as a post-sensitivity analysis (https://pan.ukbb. broadinstitute.org/). 18 Since the SAIGE analysis was a post-sensitivity analysis, we only tested the 163 lead SNPs that were significant in our discovery data set. We used a Bonferroni-corrected threshold for significance, with a p-value less than 6.13E-05 (p < 6.13E-05 = 5.00E-02/163/5).
We used FUMA, a web-based platform for comprehensive functional mapping of genetic variants, to identify independent SNPs through linkage disequilibrium (LD, r 2 ) clumping for a genomic region. 19 We selected independent and lead SNPs based on the following criteria; p < 5.00 � 10 −8 for genome-wide significant SNPs, r 2 ≤ 0.6 (for independent SNPs) and r 2 ≤ 0.1 (for lead SNPs) within the 250-Kb flanking region. We performed conditional analysis using GCTA software and examined all SNPs within the 1-Mb flanking region.
We used the LDSC regression software (LDSC v1.0.1) to calculate genetic correlation and heritability. 20 We used GWAS summary statistics for each cluster and for other traits from GWASATLAS 21 (Supplementary Table S2). To account for multiple testing, we used a Bonferroni-corrected p-value threshold of p < 9.09 � 10 −4 = 0.05/55 to determine the significance of genetic correlations.
The gene-set enrichment analysis was performed using the marker analysis of genomic annotation (MAGMA), integrated in FUMA. We used the p-values of GWAS summary statistics as input for gene-based analysis via MAGMA, 28 30 and MAGMA for gene-set enrichment analysis. Tissue specificity analysis was performed on 54 cell-type-specific expression profiles obtained from the GTEx portal. Associations of gene sets with asthma clusters were analysed using MAGMA. 28 For all gene sets, we calculated competitive p-values to test whether the combined effect of genes in a gene set was significantly greater than that of the same number of randomly selected genes. Gene sets that survived the Bonferroni-correction (p < 3.23 � 10 −6 = 0.05/15481) were reported.

| Clustering analysis with asthma onset age and predicted FEV1%
We conducted k-means clustering on 36,086 asthma patients based on their onset age and predicted FEV1% values. We identified four clusters, and the scatter plot in Supplementary Figure S3 shows the differences in the two variables used for clustering in each cluster. The early onset asthma normalLF (n = 8172) and early onset asthma reducedLF (n = 8925) had average onset ages of 15.9 and 12.2 years, respectively, while those of late-onset asthma normalLF (n = 12,481) and late-onset asthma reducedLF (n = 6508) were 46.2 and 46.3 years, respectively ( Table 1). The average of predicted FEV1% and FEV1/FVC ratio values of early onset asthma reducedLF (73.9% and 68.7%, respectively) and late-onset asthma reducedLF (66.5% and 67.3%, respectively) were below the normal range, while those of early onset asthma normalLF (100.6% and 75.95%, respectively) and late-onset asthma normalLF (94.66% and 75.46%, respectively) were within the normal range ( Table 1).
As expected, early onset asthma clusters had a higher comorbidity rate with allergic diseases such as hay fever or eczema compared to late-onset clusters (56.5% in early onset asthma normalLF and 53.3% in early onset asthma reducedLF vs. 39.4% in late-onset asthma normalLF and 33.3% in late-onset asthma reducedLF ) ( Table 1 and Supplementary   Table S27). Eosinophil counts, current smoker ratios, and medication user ratios were higher in the reduced lung function clusters (early onset asthma reducedLF and late-onset asthma reducedLF ) than in the normal lung function clusters (early onset asthma normalLF and lateonset asthma normalLF ) ( Table 1 and Supplementary Table S27).
Furthermore, general asthma symptoms such as wheezing or whistling, cough and sputum on most days, and shortness of breath while walking on level ground were more frequent in the reduced lung function clusters than in the normal lung function clusters for the same onset age clusters, and these symptoms were overall more common in lateonset clusters than in early onset clusters (Table 1 and Supplementary Table S27).

| Genome-wide associations in all asthma samples and four clusters
All significant genome-wide association signals from GWASs on All asthma set and four asthma clusters are presented as Manhattan plots ( Figure 1) and listed in Supplementary Tables S3-S7. We identified a total of 163 lead SNPs, including 153 SNPs in All asthma, 39 SNPs in early onset asthma normalLF , 71 SNPs in early onset asthma reducedLF , 16 SNPs in late-onset asthma normalLF , and 5 SNPs in lateonset asthma reducedLF (Table 2 and Supplementary Table S8). 19 Among these 163 SNPs, 51 lead SNPs were only significant in one of the four clusters, referred to as cluster-specific SNPs in our study: 6 SNPs in early onset asthma normalLF , 38 SNPs in early onset asthma reducedLF , 6 SNPs in late-onset asthma normalLF , and 1 SNP in late-onset asthma reducedLF (Tables 3 and 4, Supplementary Table S8).
Additionally, we found that 33 SNPs showed multiple associations in more than one cluster (22 SNPs in two early onset clusters, 8 in three clusters, and 3 in all four clusters; Supplementary Table S8).
The plotted p-values of associated signals showed that early onset asthma reducedLF had more significant association signals with smaller p-values than the other clusters (Supplementary Figure S4).
Pairwise plotting of the association p-values within the early onset or late-onset clusters revealed that early onset asthma reducedLF had more significant p-values than early onset asthma normalLF , and lateonset asthma normalLF had more significant p-values than late-onset asthma reducedLF (Supplementary Figure S5).

| Novel association signals
To identify novel signals among the 163 lead SNPs, we examined the association studies on asthma in the GWAS Catalogue and found 231 SNPs that were previously reported to be associated with asthma (Supplementary Table S10). Out of 163 lead SNPs, 149 SNPs, including 62 sentinel and 87 proxies, have been previously reported.
Of the total 163 lead SNPs, 14 were identified as novel signals that had not been previously reported (Table 3). To confirm the novelty of these SNPs, conditional analysis with GCTA was performed, validating them as previously unidentified (Supplementary   Table S9). 31 Of these 14 SNPs, 5 were located at novel loci for asthma that had not been reported yet (Table 3) Table S27). The letter "b" indicates significant differences between EON and EOR; "c", EON and LON; "d", EON and LOR; "e", EOR and LON; "f", EOR and LOR; and "g", LON and LOR.

| Validation of association signals and clusterspecific signals
To  Tables S11 and   S12). However, among 14 novel signals, 6 available SNPs were not replicated in this meta-analysis (Table 3 and Supplementary Tables S11 and S12).
Reproducing GWASs with clusters using summary statistics from previous studies is not feasible. To reproduce GWAS with clusters, we performed k-means clustering on the unused British and Irish replication set of the UK Biobank (2592 asthma patients) and conducted association analysis in four clusters and controls (n = 13,285): 467 early onset asthma normalLF , 794 early onset asthma reducedLF , 522 late-onset asthma normalLF , and 809 late-onset asthma reducedLF . We conducted a meta-analysis of the association results from both the unused UK Biobank sample clusters and the discovery sample clusters. A total of 51 reproducible signals were identified after the application of three criteria: P < 5E-8 in discovery clusters, p < 0.05 in replication clusters, and p values from the meta-analysis less than P from the discovery or replication clusters. We identified reproducible associations of 9 out of 39 in early onset asthma normalLF , 38 out of 72 in early onset asthma reducedLF , 2 out of 16 in late-onset asthma normalLF , and 2 out of 5 in late-onset asthma reducedLF (Supplementary Tables S13 and 14). Among the cluster-specific novel signals, rs205002 in PPRT1 was consistently reproducible in early onset asthma reducedLF of the replication set and rs113457465 in HLA-DQB1 was reproducible in early onset asthma normalLF of the replication set (Supplementary Table S14).
Despite the relatively small size of the replication set, we observed that more than half of the cluster-specific SNPs were  Additionally, to address potential issues of hidden ancestry and unbalanced case-control, we conducted the SAIGE analysis using the 163 lead SNPs that were significant in our discovery data set as a post-sensitivity analysis. 18  of 153 in all asthma data set; see Supplementary Table S28).
Therefore, our findings suggest that at least 93.5% of our lead SNPs are unlikely to be affected by false positive errors due to the unbalanced case-control issue and hidden ancestry.

| Heritability and genetic correlation between clusters and other traits
We used LDSC to estimate the heritability of All asthma and four clusters using the summary statistics obtained in this study. The heritability of clusters ranged from 0.079 to 0.212, while that of All asthma set was estimated to be 0.132 (Table 2). Consistent with previous studies, we observed higher heritability in early onset clusters (h 2 = 0.176 in early onset asthma normalLF and h 2 = 0.212 in early onset asthma reducedLF ) compared to late-onset clusters (h 2 = 0.079 in late-onset asthma normalLF and h 2 = 0.130 in late-onset asthma reducedLF ). 11 We utilized LDSC to investigate the degree of genetic risk factor sharing among clusters (Figure 2 and Supplementary Table S15). The clusters exhibited somewhat similar genetic correlations with All asthma: early onset asthma normalLF = 0.82, early onset asthma reducedLF = 0.92, late-onset asthma normalLF = 0.85, and lateonset asthma reducedLF = 0.79. We also examined the extent of genetic risk factor sharing between pairwise clusters. The highest genetic correlations were observed between late-onset asthma normalLF and early onset asthma reducedLF (r g = 0.76), while the lowest was between early onset asthma normalLF and late-onset asthma reducedLF (r g = 0.37). The genetic correlation between late-onset clusters (lateonset asthma normalLF and late-onset asthma reducedLF , r g = 0.54) was lower than that between early onset clusters (early onset asthma-normalLF and early onset asthma reducedLF , r g = 0.76), and the genetic correlation between reduced lung function clusters was higher (early onset asthma reducedLF and late-onset asthma reducedLF , r g = 0.76) than that between normal lung function clusters (early onset asthma-normalLF and late-onset asthma normalLF , r g = 0.63).
We also examined the genetic correlations of clusters with nine asthma-related traits. As expected, we observed a significant negative genetic correlation between lung function traits, such as FEV1/ FVC ratio and PEF only in reduced lung function clusters (early onset asthma reducedLF r g = −0.35 and late-onset asthma reducedLF r g = −0.46 with FEV1/FVC ratio; early onset asthma reducedLF r g = −0.38, and late-onset asthma reducedLF r g = −0.51 with PEF). Interestingly, we found a significant genetic correlation with BMI in early onset asthma normalLF and late-onset asthma reducedLF but in opposite directions (early onset asthma normalLF r g = −0.11 and late-onset asthma reducedLF r g = 0.10). No significant genetic correlations were observed with other asthma-related traits, such as shortness of breath, chest pain, wheezing, and smoking.

| Gene-set enrichment analysis for asthma clusters
Tissue specificity and gene analysis of cluster GWAS signals were investigated through gene-based GWAS. A detailed description of these analyses is provided in the 'Supplementary results' section. Gene set analyses were conducted using KEGG, Reactome, BioCarta, and GO data from MSigDB, and the results are presented in Tables S16, S17, and S18. 30 Based on multiple correction (p < 3.23 � 10 −6 ), 11 T A B L E 2 Summary of our findings and estimation of cluster heritability. Cluster only a 10 ---- Did not satisfy the criteria of 'GWAS significant in All asthma' but was significant only in specific clusters.
KIM ET AL.

5.29E-03
Note: Genome-wide significant signals are stated in bold, and the specificity column was determined by whether the genome-wide significance was satisfied for each study. The SNP reported by the same locus was described on the Reported SNP and the LD value with that SNP was displayed in the R 2 column. The italic words indicate the name for human genes. a All asthma, only significant in all asthma; EON-LOR, Significant for specific clusters only; Multiple, Significant in 3 clusters; Significance of all asthma was not considered when determining cluster specificity.
b Locus, newly discovered locus; Signal, newly discovered signal from reported locus.

| DISCUSSION
Applying k-means clustering to data at onset age and predicted FEV1% from 36,086 asthma patients in the UK Biobank yielded four asthma clusters. The cluster GWASs identified 163 lead SNPs, of which 14 represent novel association signals, and 51 were specific to the clusters.
We propose that the heterogeneity of asthma has impeded the identification of genetic factors in previous asthma GWASs. Therefore, we first classified patients with asthma into clusters before performing GWAS, which allowed us to identify 10 additional lead SNPs with genome-wide significance in specific clusters that were not significant in All asthma. Among these SNPs, two (rs2735102 in early onset asthma normalLF and rs4745723 in early onset asthma reducedLF ) have not been reported before. identified. Among these early onset asthma reducedLF -specific signals, RORA (rs34986765), and RORC (rs3828058) were also found to be reproducible in a replication test.
In addition to the first two objectives of resolving the heterogeneity of asthma and identifying novel and cluster-specific genetic F I G U R E 2 Genetic correlations of asthma clusters within clusters and with other traits. Genetic correlations of clusters within clusters and with nine asthma-related traits were computed using cross-trait linkage disequilibrium (LD) score regression. Red and blue indicate positive and negative genetic correlations, respectively, whereas the gradient indicates the strength of the genetic correlations. Sample sizes for the analysis of each trait are provided in Supplementary Table S26. KIM ET AL.
-11 of 14 signals, this study had a third objective of elucidating genetic differences in asthma clusters. To achieve this, we performed heritability estimation, genetic correlation analysis, and a comparison of association signals among clusters. Late-onset asthma reducedLF showed the highest negative correlation with lung function traits and had the highest percentage of respiratory symptoms, such as cough, sputum production, wheezing, and shortness of breath during walking at ground level (Table 1). It also showed the highest percentage of smoking status and medication use. Moreover, late-onset asthma reducedLF had the lowest number of lead SNPs and shared the least number of lead SNPs and genes with other clusters. These findings suggest that the difference in p-values between late-onset asthma normalLF and late-onset asthma reducedLF was greater than that observed between early onset asthma normalLF and early onset asthma reducedLF . These results may indicate a distinction between lateonset asthma reducedLF and other clusters, highlighting the heterogeneity between late-onset asthma normalLF and late-onset asthma r-educedLF and the similarity between early onset asthma normalLF and early onset asthma reducedLF . Moreover, the heritability explained by common variants was highest in early onset asthma reducedLF (h 2 = 0.212), as was the number of associated lead SNPs and genes for each cluster (71 SNPs and 167 genes), and the number of significant gene sets for pathways (54 for gene set).
The box plot (Supplementary Figure S4A) and the volcano plots (Supplementary Figure S4B) clearly show that the odds ratio signals and p-values in early onset asthma normalLF and early onset asthma r-educedLF were higher than those in late-onset asthma normalLF and lateonset asthma reducedLF , with the highest values observed in early onset asthma reducedLF . These results suggest that asthma patients in the early onset asthma reducedLF were more likely to be genetically predisposed to asthma.
Shared gene sets between early onset asthma normalLF and early onset asthma reducedLF included cytokine-cytokine receptor interaction, negative regulation of T-helper 1 type immune response, type 2 immune response, regulation of type 2 immune response, regulation of isotype switching to IgE isotypes, and interleukin 1 receptor activity. These gene sets suggest that they are associated with the incidence of early onset asthma in both early onset asthma normalLF and early onset asthma reducedLF . Negative regulation of Th1 following Th2 activation and IgE binding to myeloid cells (eosinophils and mast cells) are well-known mechanisms of the immune response in asthma. 8 We have also identified gene sets specific to early onset asthma reducedLF , including leukocyte differentiation, lymphocyte activation, and cytokine production (Supplementary Table S19). These results are supported by examining early onset asthma reducedLF -specific lead SNPs corresponding to eQTL genes and functional annotations (Supplementary Tables S20, S21, and S22). Among the early onset asthma reducedLF -specific eQTL genes (Supplementary Tables S23 and S24), several are related to lymphocyte differentiation, activation, and proliferation. For instance, ITPKB and RORC, eQTL genes carrying early onset asthma reducedLF -specific lead SNPs rs3768410 and rs3828058, respectively, showed early onset asthma reducedLF -specific gene-based significances in MAGMA analysis (Supplementary Tables S25 and S26) and are involved in alpha beta T-cell activation and differentiation. Therefore, we speculate that once early onset asthma develops, further the worsening of symptoms (such as reduced lung function as in early onset asthma reducedLF ) may progress via the activated status of lymphocytes, which may be due to the genetic profile detected in early onset asthma reducedLF patients.
This study had several limitations. Firstly, the lack of replication of the 14 novel association signals was a significant limitation.
Secondly, the absence of gene sets for the two late-onset clusters, due to the inability to identify sufficient genes for gene-set enrichment analysis, was a limitation. A larger sample size of lateonset asthma patients with better phenotype characterization may be required to identify gene sets related to late-onset development.
Additionally, other factors such as the exposome may play a more significant role in late-onset asthma development. Thirdly, there were limited clinical measurements of asthma patients in the UK Biobank due to the use of only two features for clustering analysis.
This is due to the nature of the UK Biobank, which is a populationbased cohort that measures general clinical measurements of participants.
In conclusion, we have identified 14 novel lead SNPs, including five new loci from the asthma cluster GWASs. Examination of early onset asthma reducedLF -specific lead SNPs revealed four possible pathways involved in the worsening of symptoms in early onset asthma: lymphocyte activation, pathogen recognition, cytokine receptor, and lymphocyte differentiation, which was further supported by gene-set enrichment analysis results. Additionally, early onset asthma reducedLF was found to be the most genetically predisposed cluster, whereas late-onset asthma reducedLF was distinct from the other clusters. Despite the study's limitations, the findings offer new insights into the mechanisms underlying the heterogeneity of asthma.